| name | charted |
| description | Generate SVG or PNG charts from raw numbers or a CSV using charted, a zero-dependency Python chart library. Covers bar, column, line, scatter, bubble, pie, polar_area, radar, area, box, histogram, heatmap, gantt, combo, and sankey, plus multi-series data, named palettes, and a CLI. Use when asked to plot, chart, or visualize data and produce an image file. |
charted
charted turns lists of numbers (or a CSV) into a clean SVG chart. The core library has no runtime dependencies. PNG export pulls in cairosvg as an opt-in extra.
Install
pip install 'charted[png]'
Use plain pip install charted if you only need SVG.
Generate a chart in Python
from charted import BarChart
chart = BarChart(
title="Sales by Quarter",
data=[120, 180, 210, 150],
labels=["Q1", "Q2", "Q3", "Q4"],
width=700,
height=400,
)
chart.save("sales.svg")
chart.save("sales.png")
save() picks the format from the file extension. Every chart class takes the same
core arguments: data, labels, title, width, height.
Generate a chart from the CLI
No Python needed. The first CSV column is the x-axis labels, every other column is a data series.
python -m charted create bar output.svg --data sales.csv
python -m charted create bar output.svg --data sales.csv --title "Q3 Sales" --width 900 --height 400
python -m charted batch input_data/ output_svg/
Use --transpose when the CSV is laid out sideways (one series per row, x values across the header row).
Pick a chart type by data shape
| Data shape | Use |
|---|
| Values across categories | bar, column |
| Values over a continuous x | line, area |
| Two paired numeric variables | scatter (add a size for bubble) |
| Parts of a whole | pie, polar_area |
| Several metrics per item | radar |
| Distribution of one variable | histogram, box |
| Grid of two categories | heatmap |
| Tasks over time | gantt |
| Bars and a line together | combo |
| Flows between nodes | sankey |
All chart types: bar, column, line, scatter, bubble, pie, polar_area,
radar, area, box, histogram, heatmap, gantt, combo, sankey.
Import any of them from charted.charts, for example
from charted.charts import LineChart, PieChart, ScatterChart.
Multi-series data
Pass a list of lists as data and name each series with series_names. Bar and
column charts group side by side by default; set x_stacked=True to stack them.
from charted.charts import ColumnChart
ColumnChart(
title="Revenue vs Expenses",
data=[[120, 180, 210], [80, 95, 110]],
labels=["Q1", "Q2", "Q3"],
series_names=["Revenue", "Expenses"],
).save("compare.svg")
Themes and palettes
Built-in theme presets: light, dark, high-contrast. Pass one as theme=.
BarChart(data=[120, 180, 210], labels=["Q1", "Q2", "Q3"], theme="dark").save("dark.svg")
Named palettes turn into a list of hex colors via resolve_palette(name), passed as colors=:
from charted import resolve_palette
BarChart(data=[1, 2, 3], colors=resolve_palette("viridis")).save("v.svg")
Named palettes: default, viridis, ocean, categorical, rainbow,
monochrome, pastel, sunset, forest, inferno, and the colourblind-safe okabe-ito.
Load a CSV in Python
from charted import load_csv, BarChart
x, y, labels = load_csv("sales.csv", x_col="Quarter", y_col="Revenue")
BarChart(data=y, labels=x, title=labels[0]).save("sales.svg")
load_json works the same way for JSON files.
Show the result
After saving, give the user the file path. SVG opens in any browser and renders
inline in Jupyter. PNG is a raster image for slides, docs, or chat. When the user
wants an image they can preview, save a .png and hand them that path.
More examples
See examples/ next to this file for short worked scripts: a CSV-to-chart pipeline,
a multi-series comparison, and a themed chart.
